Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network
Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network
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DOI：
10.1016/j.apenergy.2016.10.012
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发表时间：
2017-01-01
期刊：
影响因子：
11.2
通讯作者：
Lee, Jeonghwan
中科院分区：
文献类型：
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作者：
Kim, Youngmin;Jang, Hochang;Lee, Jeonghwan
本研究提出应用人工神经网路预测深层咸水层二氧化碳封存效率。为建立神经网络的输入输出神经元训练数据库，采用数值模拟的方法对含水层特性参数进行了敏感性分析。在此基础上，确定了影响深层咸水层CO2封存的因素及其范围，并利用残余CO2的截留指标和溶解度截留机制生成了150个具有代表性的实现作为训练数据库。采用最小均方误差（MSE）的最优结构设计了神经网络模型，并用验证样本进行了检验。结果表明，该模型具有较高的预测性能，与目标值相比，其决定系数（R-2）大于0.99，总捕集效率指数（TEI）的平均绝对百分误差（MAPE）为1.26%，均方根误差（RMSE）为0.41。作为一个现场应用，该模型也进行了评估，在块VI-1，韩国Gorae V结构的现场规模的数据。预测结果与目标值匹配良好，ANN预测与田间规模数据之间的准确性达到了大于0.96的高决定系数（R-2），4.40%的低MAPE，以及TEL的RMSE为3.58。相信新开发的人工神经网络模型可以高准确度地预测封存效率，也可以被认为是评估二氧化碳封存可行性的有用且强大的工具。深层盐水层(C)2016爱思唯尔有限公司版权所有。
This study presents the application of artificial neural network (ANN) to predict storage efficiency of CO2 sequestration in deep saline aquifers. To a create training database used as input and output neurons in ANN, sensitivity analysis of parameters considering the aquifer characteristics was performed by numerical simulation. Based on the analysis, the factors and their ranges influencing CO2 sequestration in deep saline aquifers were determined and 150 representative realizations used as a training database were generated with trapping indices of the residual CO2 and solubility trapping mechanisms. The ANN model was designed with optimum architecture minimizing the mean squared error (MSE) for testing data set and it was tested with validation samples. The results showed that the proposed ANN model had a high prediction performance with a high coefficient of determination (R-2) of over 0.99 on comparing with the target values, a low MAPE (mean absolute percentage error) of 1.26%, and RMSE (root mean square error) of 0.41 for TEI (total trapping efficiency index). As a field application, the model has also been evaluated with the field scale data on the Gorae V structure in Block VI-1, Korea. The results of prediction were well matched with the targeted values and accuracy between the ANN predictions and field scale data was achieved with a high coefficient of determination (R-2) of more than 0.96, a low MAPE of 4.40%, and RMSE of 3.58 for TEL From these results, it is believed that the newly developed ANN model can predict storage efficiency with high accuracy and it can also be considered as a useful and robust tool to evaluate the feasibility of CO2 sequestration in deep saline aquifers. (C) 2016 Elsevier Ltd. All rights reserved.